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Rice

Table 5 Summary of multinomial logistic regression for variables characterising the different rice quality clusters. Cluster 1 is not shown in Table 5 because it is the reference cluster. Table 5 indicates the multinomial log-odds that samples represented in Cluster 2 or in Cluster 3, were compared to reference cluster 1 to calculate every unit increase or decrease in the different grain quality attributes included in the multinomial logistic regression model

From: Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm

Grain quality attribute

Estimate

Cluster 2

Cluster 3

Intercept

−25.51

(15.27)

3.61

(0.08)***

AC (%)

0.08

(0.30)

−9.17

(2.94)***

G’trough

−0.33

(0.19)*

−1.48

(0.93)

BD

0.06

(0.03)**

0.72

(3.55)

S1

−0.88

(0.22)***

−1.37

(3.20)

tan (δ) at G’max

0.55

(0.14)***

0.21

(11.71)

S3

−2.84

(0.84)***

−0.12

(0.66)

GT

0.54

(0.18)***

2.03

(6.97)

COH

−0.24

(0.08)***

0.01

(11.97)

G’max

−0.15

(0.06)***

0.22

(8.68)

N

70

27

  1. Note: Total N = 211; AIC = 106.20; Overall classification accuracy: 93.84%
  2. Reference category for the regression model is cluster 1 (n = 114)
  3. Standard errors of the estimates are indicated in parentheses
  4. * p < 0.1, ** p < 0.05, *** p < 0.01.
  5. Goodness-of-fit statistics: Residual Deviance = 66.20; Degrees of freedom = 18
  6. –2Log-likelihood: The intercept-only model: 405.87; The final model: 66.20; χ2 = 339.66; p < 0.01
  7. Pseudo-R2: McFadden = 0.84; Cragg & Uhler = 0.94; Cox & Snell = 0.80
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